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SSRN 2023年

作者： Zhao, Zifeng Liu, Ziyu Chen, Lin Sun, Qingqing Liu, Hongye Sun, Yabin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China Jiashan Fudan Institute Zhejiang Province Jiaxing314100 China Department of Electrical Engineering East China Normal University Shanghai200241 China

Abstract Through-Silicon Via (TSV) is a key technology of three-dimensional (3D) integration. However, it is easy to have voids in the Cu filling process of TSV. Additives and optimized filling process are used to solve this problem. However, the comprehensive analysis of these affecting parameter needs many experiments and cost much time. Thus, the finite element analysis (FEA) for the mass transferring of electroplating is carried out by the COMSOL software in the work. The filling processes with different inhibitor concentrations are first studied and the most suitable density of 64mol/m2 is obtained. However, high inhibitor concentration will lead to much lower plating speed. Thus, the effect of filling process such as current density and plating bath flow velocity on the voids are investigated under the optimized concentrations above. The best filling quality is obtained at the current density of 1.4 ASD and the bath stirring velocity of 0.4 m/s. The underlying growth mechanism of these influencing factors are also analyzed and revealed. © 2023, The Authors. All rights reserved.

关键词： Copper

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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Neural network detection of shilling attack based on user rating history and latent features

Neural network detection of shilling attack based on user ra...

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2019 International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2019

作者： Yuan, Wanqiao Xiao, Yingyuan Jiao, Xu Ming, Yidong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781728150949

The recommendation system is a significant field to solve the problem of information overload today. However, the recommendation system relies heavily on the user's behavior information, which is easy to suffer attacks. Therefore, how to prevent the recommendation system from effecting by the attacks is a major issue in the research field of protection recommendation system. In response to the above questions, we found that the shilling attack has the features of short rating history, large number of rating and other potential features compared with normal user profiles. Therefore, we propose a neural network structure to learn the latent features between users and items and we construct shilling attack detection model based on neural network. The experimental results show that the detection method of this paper has achieved the expected goal. © 2019 IEEE.

关键词： Recommender systems

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An energy prediction model for cloud data centers through performance counter

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International Journal of Performability Engineering 2019年第11期15卷 2962-2971页

作者： Meng, Sa Sun, Peng Luo, Jie Xu, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China

In recent years, with an increased environmental protection concern, the carbon footprints of large-scale cloud data centers have come into public view. Energy efficiency has become a key indicator for these data centers. Management personnel in cloud data centers should know the relationship between the workload patterns and the energy consumption of the infrastructure in order to optimize energy efficiency. In this paper, we first discuss the energy consumption problems of cloud data centers and summarize the related work. Then, we purpose an energy prediction model to estimate the energy consumption of servers in cloud data centers based on performance counters of their processors. Afterward, the proposed model is tested and analyzed under a wide selection of benchmarks, including SPEC2006, I/Ozone, and Netperf. Finally, through analyzing the results of the contrast experiments, it is shown that the proposed energy prediction model can predict energy consumption of cloud servers with high accuracy. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Energy utilization

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Research on Assistant Diagnostic Method of TCM Based on BERT

Research on Assistant Diagnostic Method of TCM Based on BERT

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chuanjie Xu Feng Yuan Shouqiang Chen Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China School of Information Engineering Shandong Management University Jinan China Center of Hear of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9781728194202

Traditional Chinese medicine (TCM) auxiliary diagnosis is a systematic diagnosis platform that uses computer modeling technology to assist TCM doctors in recording diseases, providing on time diagnoses, writing prescriptions, performing tele-medicine, and supporting medical teaching. This study proposes a Bidirectional Encoder Representations from Transformers TCM auxiliary diagnosis model using 20,000 items of TCM records. These records were collected from the outpatient clinic of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine. Specifically, our model aims to predict final diagnosis while taking TCM symptoms as inputs; for example, when we input relief of chest tightness but persistent tiredness and sluggishness, the model provides a diagnosis of chest paralysis. An experiment was conducted on these real-world Chinese medical data. Results show that our model achieves state-of-the-art performance. Hence, our proposed model can effectively use the information from the four diagnostic procedures in the TCM text.

关键词： Systematics Computational modeling Education Predictive models Writing Transformers Data models

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Construction of a Cloud Scenario Knowledge Graph for Cloud Service Market

Construction of a Cloud Scenario Knowledge Graph for Cloud S...

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IEEE International Conference on software Engineering and Service sciences (ICSESS)

作者： Shiqi Zhang Xiaoqiang Liu Bo Xu Lizhi Cai Yun Hu School of Computer Science and Technology Donghua University & Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Fast-growing cloud computing technology makes cloud application scenarios more diversified and difficult to choose. A knowledge recommending system is an effective way to reach automatic recommendation services. In this paper, we constructed a cloud scenario knowledge graph (CSKG) for cloud service market to assist cloud knowledge management and provided a web service interface for recommended applications. First a cloud scenario domain ontology was defined based on product knowledge of cloud suppliers and experience of domain experts. Then the BiLSTM-CRF model and Rule-based method were combined to automatically extract structured knowledge from multi-source and heterogeneous texts, where we proposed a tagging schema combined with entity extraction and relation classifier. Finally, a cloud scenario knowledge graph was constructed and visualized, several application examples based on the CSKG service were suggested.

关键词： Knowledge engineering Cloud computing Visualization Web services Tagging Ontologies software engineering

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AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

arXiv

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arXiv 2024年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Software Jilin University Jilin Changchun130012 China School of Life Sciences Jilin University Jilin Changchun130012 China Department of Computer Science Columbia University 116th and Broadway New York CityNY10027 United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun130103 China Changchun Wenli High School Jilin Changchun130062 China

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://***/supp/or https://***/Kewei2023/AMPCliff-generation. © 2024, CC BY-NC-SA.

关键词： Deep learning

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EPI-Guided Cost Construction Network for Light Field Disparity Estimation

EPI-Guided Cost Construction Network for Light Field Dispari...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tun Wang Rongshan Chen Ruixuan Cong Da Yang Zhenglong Cui Fangping Li Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing P.R. China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou P.R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

Recent learning-based light field (LF) disparity estimation methods construct cost volume by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets. They only use the visual information of SAIs and lose the geometry of LF. In this paper, we design a simple network that can cleverly integrate EPI features with cost volume to estimate the disparity. Firstly, we propose an efficient EPI extraction module to use abundant line characteristics. Secondly, we offer an EPI-Cost volume construction module that can create volume guided by the EPI line and the color consistency of images. Finally, after completing it, we adopt an inter-volume fusion module to considerably correlate the validity of EPI lines in both directions. Experimental results show the proposed method achieves state-of-the-art performance in the quantitative and qualitative evaluation of the UrbanLF-Syn dataset.

关键词：

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An Identification Method of Image-based for Output Pattern of Metamorphic Relation in Burnup Calculation Program 3

An Identification Method of Image-based for Output Pattern o...

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3rd International Academic Exchange Conference on science and technology Innovation, IAECST 2021

作者： Fu, Shengfu Yang, Xiaohua Li, Meng Wang, Fan Eng. and Technol. Res. Ctr. of Software Eval. and Test. for Intellectual Equipment of Hunan Province Hunan Prov. Hengyang421001 China CNNC Key Laboratory on High Trusted Computing Hunan Prov. Hengyang421001 China School of Computer Science and Technology University of South China Hunan Hengyang421001 China

ISBN: (纸本)9781665402675

It is challenging to verify the design and analysis software adequately for nuclear power areas because of the inflated cost, time-consuming benchmark problems development, and few verification examples. This situation is called the test oracle problem. Metamorphic testing cleverly combines the specific properties of physical equations and numerical algorithms with software verification. Without constructing numerical solutions or verification examples, it verifies the software by checking whether the input and output of multiple executions meet the metamorphic relations. Since metamorphic relation is the nature rules of physical models and numerical algorithms, it is not restricted to specific code implementation technology. Therefore, it is no doubt that metamorphic testing is a promising technique to alleviate the test oracle *** relation is the key to metamorphic testing, which consists of input pattern and output pattern. The former can be obtained through manual analysis by industry experts, while the latter does not have a system identification method. An output pattern identification method based on the solution figure has been introduced here. Specifically, the input pattern is received by artificially analyzing the physical property of the program under test. Next, a group of test inputs has been generated according to such patterns. The calculation results are received after those inputs drive the program. Moreover, the function form of the output pattern is guided by the image of the results. Finally, metamorphic relation is constructed by input pattern and output pattern. For a demonstration of this method's details, a nuclide burnup calculation code is employed here, namely NUIT. By investigating the resulting image of 3820 isotopes, their output pattern classification models have been established according to the burnup depth and nuclide density. It includes four categories and seventeen sub-categories. The verification of other burnup

关键词： Verification

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Modeling Topic-Based Human Expertise for Crowd Entity Resolution

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Journal of computer science & technology 2018年第6期33卷 1204-1218页

作者： Sai-Sai Gong Wei Hu Wei-Yi Ge Yu-Zhong Qu State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Science and Technology on Information Systems Engineering Laboratory Nanjing 210007 China

Entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world *** ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate orerroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found thatcorrectly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number ofthem assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have variedexpertise on different topics （e.g., music versus sport）. In this paper, we deal with crowd ER in the Semantic Web *** identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similartask clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model thatcomputes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluationresults on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposedmodel achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.

关键词： entity resolution crowdsourcing human expertise topic modeling task similarity

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